SMG-bud-count-BB94-GM6001.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sw_plotting import change_bar_width, plotCountBar\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>bud_count</th>\n",
       "      <th>time</th>\n",
       "      <th>groups</th>\n",
       "      <th>group_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20210125-mouse1-BB94-0uM-001_8bit.tif</td>\n",
       "      <td>4</td>\n",
       "      <td>day0</td>\n",
       "      <td>control</td>\n",
       "      <td>control_day0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210125-mouse1-BB94-0uM-002_8bit.tif</td>\n",
       "      <td>4</td>\n",
       "      <td>day0</td>\n",
       "      <td>control</td>\n",
       "      <td>control_day0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20210125-mouse1-BB94-0uM-003_8bit.tif</td>\n",
       "      <td>3</td>\n",
       "      <td>day0</td>\n",
       "      <td>control</td>\n",
       "      <td>control_day0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210125-mouse1-BB94-0uM-004_8bit.tif</td>\n",
       "      <td>2</td>\n",
       "      <td>day0</td>\n",
       "      <td>control</td>\n",
       "      <td>control_day0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210125-mouse1-BB94-0uM-005_8bit.tif</td>\n",
       "      <td>4</td>\n",
       "      <td>day0</td>\n",
       "      <td>control</td>\n",
       "      <td>control_day0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               file_name  bud_count  time   groups  \\\n",
       "0  20210125-mouse1-BB94-0uM-001_8bit.tif          4  day0  control   \n",
       "1  20210125-mouse1-BB94-0uM-002_8bit.tif          4  day0  control   \n",
       "2  20210125-mouse1-BB94-0uM-003_8bit.tif          3  day0  control   \n",
       "3  20210125-mouse1-BB94-0uM-004_8bit.tif          2  day0  control   \n",
       "4  20210125-mouse1-BB94-0uM-005_8bit.tif          4  day0  control   \n",
       "\n",
       "       group_id  \n",
       "0  control_day0  \n",
       "1  control_day0  \n",
       "2  control_day0  \n",
       "3  control_day0  \n",
       "4  control_day0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bud count data for control, BB-94 or GM-6001 treated glands\n",
    "\n",
    "# day 0\n",
    "df = pd.read_csv('../data/SMG-bud-count-BB94-GM6001/20210125-BB94-GM6001-E12p5-or-E13-0d-output-824389-budCount.txt')\n",
    "df.columns = ['file_name', 'bud_count']\n",
    "df['time'] = ['day0'] * len(df)\n",
    "df['groups'] = ['control']*5 + ['BB-94']*5 + ['control']*5 + ['GM-6001']*5\n",
    "df['group_id'] = [df.groups.tolist()[i] + '_' + df.time.tolist()[i] for i in range(len(df))]\n",
    "df0 = df\n",
    "\n",
    "df = pd.read_csv('../data/SMG-bud-count-BB94-GM6001/20210126-BB94-GM6001-E12p5-or-E13-1d-output-387588-budCount.txt')\n",
    "df.columns = ['file_name', 'bud_count']\n",
    "df['time'] = ['day1'] * len(df)\n",
    "df['groups'] = ['control']*5 + ['BB-94']*5 + ['control']*5 + ['GM-6001']*5\n",
    "df['group_id'] = [df.groups.tolist()[i] + '_' + df.time.tolist()[i] for i in range(len(df))]\n",
    "df1 = df\n",
    "\n",
    "df = pd.read_csv('../data/SMG-bud-count-BB94-GM6001/20210127-BB94-GM6001-E12p5-or-E13-2d-output-129975-budCount.txt')\n",
    "df.columns = ['file_name', 'bud_count']\n",
    "df['time'] = ['day2'] * len(df)\n",
    "df['groups'] = ['control']*5 + ['BB-94']*5 + ['control']*5 + ['GM-6001']*5\n",
    "df['group_id'] = [df.groups.tolist()[i] + '_' + df.time.tolist()[i] for i in range(len(df))]\n",
    "df2 = df\n",
    "\n",
    "df = pd.concat([df0, df1, df2])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='group_id', ylabel='bud_count'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quick visulization\n",
    "sns.swarmplot(df.group_id, df.bud_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate bud count ratios\n",
    "ratio_d1to0 = df[df.time=='day1']['bud_count'].values / df[df.time=='day0']['bud_count'].values\n",
    "ratio_d2to1 = df[df.time=='day2']['bud_count'].values / df[df.time=='day1']['bud_count'].values\n",
    "\n",
    "# annoate ratios with group id\n",
    "ratios = ratio_d1to0.tolist() + ratio_d2to1.tolist()\n",
    "groups = ['ratio_d1to0']*len(ratio_d1to0) + ['ratio_d2to1']*len(ratio_d2to1)\n",
    "experiment_groups = ['control']*5 + ['BB-94']*5 + ['control']*5 + ['GM-6001']*5 + \\\n",
    "                    ['control']*5 + ['BB-94']*5 + ['control']*5 + ['GM-6001']*5\n",
    "group_id = [experiment_groups[i] + '_' + groups[i] for i in range(len(groups))]\n",
    "\n",
    "# quick visulization\n",
    "sns.swarmplot(group_id, ratios)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Important to note that day 1 to day 0 ratio is very sensitive to the initial stage,\n",
    "# and thus has high variation between experiments\n",
    "#\n",
    "# To simplify the plot, only plot the day 2 to day 0, similar as in integrin blocking\n",
    "# \n",
    "ratios = df[df.time=='day2']['bud_count'].values / df[df.time=='day0']['bud_count'].values\n",
    "\n",
    "# annoate ratios with group id\n",
    "group_id = ['Control']*5 + ['BB-94']*5 + ['Control']*5 + ['GM-6001']*5\n",
    "\n",
    "# quick visulization\n",
    "sns.swarmplot(group_id, ratios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "  Multiple Comparison of Means - Tukey HSD, FWER=0.05  \n",
      "======================================================\n",
      " group1  group2 meandiff p-adj   lower   upper  reject\n",
      "------------------------------------------------------\n",
      "  BB-94 Control   5.5417  0.001  3.8882  7.1951   True\n",
      "  BB-94 GM-6001   1.6667 0.0929 -0.2426  3.5759  False\n",
      "Control GM-6001   -3.875  0.001 -5.5285 -2.2215   True\n",
      "------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.09286592 0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fad73175890>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(ratios, group_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 240x420 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Swarm and bar plot of different collagenase concentrations\n",
    "\n",
    "outputPrefix = 'SMG-BB94-GM6001-bud-ratios-day2-to-0'\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \".svg\"\n",
    "\n",
    "fig_width=0.8\n",
    "fig_height=1.4\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n",
    "\n",
    "ax = sns.swarmplot(x=group_id, y=ratios,\n",
    "                   color=\"blue\",\n",
    "                   size=2.0,\n",
    "                   alpha=.4)\n",
    "\n",
    "ax = sns.barplot(x=group_id, y=ratios,\n",
    "                 color=\".7\", alpha=1.0,\n",
    "                 errwidth=.7, errcolor=\"k\", capsize=.2, ci=95)\n",
    "\n",
    "plt.ylim(0, 17)\n",
    "plt.yticks([0, 5, 10, 15])\n",
    "\n",
    "plt.xlabel(None)\n",
    "plt.ylabel(\"Bud ratio 48h / 2h\")\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=60, ha=\"right\")\n",
    "\n",
    "# make the bar width narrower\n",
    "change_bar_width(ax, .6)\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "if outputFigPath is not None:\n",
    "    plt.savefig(outputFigPath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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